DL_CHW4_Q3_SajjadHashembeiki_98107077.ipynb

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   "source": [
    "# Import libraries\n",
    "import os\n",
    "import math\n",
    "import glob\n",
    "import json\n",
    "import torch\n",
    "import string\n",
    "import random\n",
    "import unicodedata\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from io import open\n",
    "from tqdm import tqdm\n",
    "from torch import optim\n",
    "from pathlib import Path\n",
    "from transformers import AdamW\n",
    "from transformers import GPT2Config\n",
    "from torch.utils.data import Dataset\n",
    "from transformers import AutoTokenizer\n",
    "from torch.utils.data import DataLoader\n",
    "from transformers import  AutoModelForCausalLM\n",
    "from __future__ import unicode_literals, print_function, division"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Part A & B"
   ]
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   "source": [
    "# Define a dataset class\n",
    "class PoemDataset(Dataset):\n",
    "    def __init__(self, file_path, tokenizer, max_length=35):\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_length = max_length\n",
    "        self.poems = self.load_poems(file_path)\n",
    "\n",
    "    def load_poems(self, file_path):\n",
    "        with open(file_path, 'r', encoding='utf-8') as file:\n",
    "            poems = file.readlines()\n",
    "        return poems\n",
    "\n",
    "    def __len__(self):\n",
    "        # Ensure the dataset size is even, as we're pairing odd and even sequences\n",
    "        return len(self.poems) // 2\n",
    "\n",
    "    def tokenize_poem(self, poem):\n",
    "        return self.tokenizer('<s>' + poem + '</s>', return_tensors=\"pt\", padding=\"max_length\", max_length=self.max_length)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        # Pair odd and even sequences\n",
    "        odd_poem = self.poems[idx * 2].strip()\n",
    "        even_poem = self.poems[idx * 2 + 1].strip()\n",
    "\n",
    "        # Tokenize input odd poem\n",
    "        input_odd = self.tokenize_poem(odd_poem)\n",
    "        input_odd_labels = self.tokenize_poem(odd_poem)\n",
    "\n",
    "        # Tokenize even poem for labels\n",
    "        labels_even = self.tokenize_poem(even_poem)\n",
    "\n",
    "        return {\"input_ids\": input_odd[\"input_ids\"], \"labels\": labels_even[\"input_ids\"], \"attention_mask\":torch.ones_like(input_odd[\"input_ids\"])}\n",
    "\n",
    "# Load tokenizer\n",
    "model_name = \"HooshvareLab/gpt2-fa\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    model_name,\n",
    "    bos_token='<s>',\n",
    "    eos_token='</s>',\n",
    "    pad_token='<pad>',\n",
    "    unk_token='<unk>')\n",
    "\n",
    "# Load Ferdousi poems for fine tuning\n",
    "file_path = '/kaggle/input/poemssss/ferdousi.txt'\n",
    "poem_dataset = PoemDataset(file_path=file_path, tokenizer=tokenizer, max_length=35)\n",
    "\n",
    "\n",
    "# Split the dataset into train and test sets\n",
    "train_size = int(0.8 * len(poem_dataset))\n",
    "test_size = len(poem_dataset) - train_size\n",
    "train_dataset, test_dataset = torch.utils.data.random_split(poem_dataset, [train_size, test_size])\n",
    "\n",
    "# DataLoader batch size setup\n",
    "batch_size = 128\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of pairs(BEYTs): 49608\n",
      "==================================================\n",
      "Input:: <s>چنان ساخت روشن جهان آفرین</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>\n",
      "Label:: <s>که پنهان نکرد اژدها را زمین</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "print('Number of pairs(BEYTs):',len(poem_dataset))\n",
    "print('=' * 50)\n",
    "\n",
    "# Print a sample datapoint\n",
    "print('Input::',tokenizer.decode(poem_dataset.__getitem__(4960)['input_ids'].squeeze()))\n",
    "print('Label::',tokenizer.decode(poem_dataset.__getitem__(4960)['labels'].squeeze()))\n",
    "print('=' * 50)"
   ]
  },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Jan  7 18:20:35 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   CUDA Version: 12.2     |\n",
      "|-----------------------------------------+----------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                      |               MIG M. |\n",
      "|=========================================+======================+======================|\n",
      "|   0  Tesla P100-PCIE-16GB           Off | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   34C    P0              25W / 250W |      0MiB / 16384MiB |      0%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                            |\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
      "|        ID   ID                                                             Usage      |\n",
      "|=======================================================================================|\n",
      "|  No running processes found                                                           |\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part C"
   ]
  },
  {
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   "execution_count": 7,
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    "execution": {
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   "source": [
    "# Set Device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") \n",
    "\n",
    "# Load GPT-2 Farsi model and move to Device\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name).to(device)"
   ]
  },
  {
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   "execution_count": 8,
   "metadata": {
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    },
    "execution": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:429: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "Epoch 1: 100%|██████████| 311/311 [03:13<00:00,  1.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Average Loss: 1.5959391053466552\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Validation: 100%|██████████| 78/78 [00:19<00:00,  4.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Validation Loss: 1.4389127355355482\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|██████████| 311/311 [03:12<00:00,  1.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2, Average Loss: 1.4148794623433203\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Validation: 100%|██████████| 78/78 [00:19<00:00,  4.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Validation Loss: 1.4123997214512947\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|██████████| 311/311 [03:12<00:00,  1.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3, Average Loss: 1.3685466675896354\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Validation: 100%|██████████| 78/78 [00:19<00:00,  4.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Validation Loss: 1.3965735466052325\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|██████████| 311/311 [03:12<00:00,  1.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4, Average Loss: 1.3202510443531048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Validation: 100%|██████████| 78/78 [00:19<00:00,  4.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Validation Loss: 1.4020713109236498\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Set optimizer\n",
    "optimizer = AdamW(model.parameters(), lr=5e-4, eps=1e-8)\n",
    "\n",
    "# Set number of epochs\n",
    "num_epochs = 4\n",
    "\n",
    "# Loss lists for plot\n",
    "train_loss = []\n",
    "validation_loss = []\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "\n",
    "    for batch in tqdm(train_dataloader, desc=f\"Epoch {epoch + 1}\"):\n",
    "        inputs = batch[\"input_ids\"].to(device)\n",
    "        labels = batch[\"labels\"].to(device)\n",
    "        b_masks = batch[\"attention_mask\"].to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        model.zero_grad()\n",
    "        outputs = model(inputs, labels=labels, attention_mask=b_masks)\n",
    "        loss = outputs.loss\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        total_loss += loss.item()\n",
    "\n",
    "    average_loss = total_loss / len(train_dataloader)\n",
    "    train_loss.append(average_loss)\n",
    "    print(f\"Epoch {epoch + 1}, Average Loss: {average_loss}\")\n",
    "\n",
    "    # Validation loop\n",
    "    \n",
    "    model.eval()\n",
    "    total_test_loss = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for batch in tqdm(test_dataloader, desc=\"Validation\"):\n",
    "            inputs = batch[\"input_ids\"].to(device)\n",
    "            labels = batch[\"labels\"].to(device)\n",
    "            b_masks = batch[\"attention_mask\"].to(device)\n",
    "\n",
    "            outputs = model(inputs, labels=labels, attention_mask=b_masks)\n",
    "            loss = outputs.loss\n",
    "\n",
    "            total_test_loss += loss.item()\n",
    "\n",
    "    average_test_loss = total_test_loss / len(test_dataloader)\n",
    "    validation_loss.append(average_test_loss)\n",
    "    print(f\"Average Validation Loss: {average_test_loss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T13:46:04.415409Z",
     "iopub.status.busy": "2024-01-07T13:46:04.415012Z",
     "iopub.status.idle": "2024-01-07T13:46:04.662608Z",
     "shell.execute_reply": "2024-01-07T13:46:04.661368Z",
     "shell.execute_reply.started": "2024-01-07T13:46:04.415377Z"
    }
   },
   "outputs": [],
   "source": [
    "# Clear cache\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As you see the model will be overfitted after 4th epoch,so I just trained for 4 epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T18:37:45.971367Z",
     "iopub.status.busy": "2024-01-07T18:37:45.970689Z",
     "iopub.status.idle": "2024-01-07T18:37:46.267984Z",
     "shell.execute_reply": "2024-01-07T18:37:46.267091Z",
     "shell.execute_reply.started": "2024-01-07T18:37:45.971329Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A Function for plot loss\n",
    "def plot_loss(train_losses, val_losses, num_epochs):\n",
    "    epochs = range(1, num_epochs + 1)\n",
    "    \n",
    "    plt.plot(epochs, train_losses, label='Training Loss')\n",
    "    plt.plot(epochs, val_losses, label='Validation Loss')\n",
    "    \n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "# Call the function to plot the loss\n",
    "plot_loss(train_loss, validation_loss, num_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a Poem with random prompts, you can generate your poem with following function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T18:54:05.754376Z",
     "iopub.status.busy": "2024-01-07T18:54:05.753633Z",
     "iopub.status.idle": "2024-01-07T18:54:05.761556Z",
     "shell.execute_reply": "2024-01-07T18:54:05.760491Z",
     "shell.execute_reply.started": "2024-01-07T18:54:05.754343Z"
    }
   },
   "outputs": [],
   "source": [
    "def generate_poem(model, tokenizer, prompt, max_length=15, top_k=1):\n",
    "    nl_index = len(prompt)\n",
    "    input_ids = tokenizer.encode('<s>' + prompt + '</s>', return_tensors=\"pt\").to(device)\n",
    "#     print(input_ids)\n",
    "#     print('-------------------------------')\n",
    "#     input_ids = tokenizer('<s>' + prompt + '</s>', return_tensors=\"pt\", padding=\"max_length\", max_length=35).to(device)\n",
    "#     print(input_ids)\n",
    "#     print('------------------------------')\n",
    "    \n",
    "    model.eval()\n",
    "    \n",
    "    # Generate poem\n",
    "    with torch.no_grad():\n",
    "        output = model.generate(input_ids, max_length=max_length, num_beams=15, no_repeat_ngram_size=1, top_k=top_k,\n",
    "                                attention_mask=torch.ones_like(input_ids),\n",
    "#                                 eos_token_id=tokenizer.eos_token_id,\n",
    "                                pad_token_id=tokenizer.pad_token_id,)\n",
    "#                                 bos_token_id=tokenizer.bos_token_id)\n",
    "\n",
    "    generated_poem = tokenizer.decode(output[0] , skip_special_tokens = True)\n",
    "    final_poem = final_gen = '\\n' +generated_poem[:nl_index] + '\\n' + generated_poem[nl_index:]\n",
    "    print('----------------------------------')\n",
    "    return final_poem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T18:54:32.605286Z",
     "iopub.status.busy": "2024-01-07T18:54:32.604404Z",
     "iopub.status.idle": "2024-01-07T18:54:33.743937Z",
     "shell.execute_reply": "2024-01-07T18:54:33.743001Z",
     "shell.execute_reply.started": "2024-01-07T18:54:32.605250Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "\n",
      "به نام خداوند\n",
      " ماه و دستگاه تاج عاجاج گنج رنج تخت\n",
      "----------------------------------\n",
      "\n",
      "دافی که میزنم باید موی\n",
      " روی جوی بوی اویویدیو\n",
      "----------------------------------\n",
      "\n",
      "رو تابلوی جاده نوشته شمشک\n",
      " رنج گنجنجگنجست بود\n",
      "----------------------------------\n",
      "\n",
      "من تنهام مثل ماهی توی تنگ\n",
      " درنگنگ جنگ چنگ ننگ\n",
      "----------------------------------\n",
      "\n",
      "بسی رنج بردم\n",
      " خون آوی ریختندآمیختندندگریگرانز\n",
      "----------------------------------\n",
      "\n",
      "نمره کامل بده آرشاک لطفا \n",
      "ادواد بادبادداد\n",
      "----------------------------------\n",
      "\n",
      "گناه دارم\n",
      " و رنج من تو مباد یاد بادواد داد\n",
      "----------------------------------\n",
      "\n",
      "بسی رنج بردم زین تمرین\n",
      " تنگنگ چنگ درنگ جنگ ننگم\n",
      "----------------------------------\n",
      "\n",
      "توانا بود\n",
      " از و به بر کموردردبم\n"
     ]
    }
   ],
   "source": [
    "print(generate_poem(model, tokenizer, 'به نام خداوند'))\n",
    "print(generate_poem(model, tokenizer, 'دافی که میزنم باید موی'))\n",
    "print(generate_poem(model, tokenizer, 'رو تابلوی جاده نوشته شمشک'))\n",
    "print(generate_poem(model, tokenizer, 'من تنهام مثل ماهی توی تنگ'))\n",
    "print(generate_poem(model, tokenizer, 'بسی رنج بردم'))\n",
    "print(generate_poem(model, tokenizer, 'نمره کامل بده آرشاک لطفا '))\n",
    "print(generate_poem(model, tokenizer, 'گناه دارم'))\n",
    "print(generate_poem(model, tokenizer, 'بسی رنج بردم زین تمرین'))\n",
    "print(generate_poem(model, tokenizer, 'توانا بود'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In following cell I generate the 2nd MESRA for each MESRA of the test set (I just print 40 of them)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-01-07T18:49:13.349070Z",
     "iopub.status.busy": "2024-01-07T18:49:13.348187Z",
     "iopub.status.idle": "2024-01-07T18:49:20.520805Z",
     "shell.execute_reply": "2024-01-07T18:49:20.519937Z",
     "shell.execute_reply.started": "2024-01-07T18:49:13.349027Z"
    },
    "id": "xJ_gIERFlv6f",
    "outputId": "43bca5f9-9313-4535-ddfe-3b3d9747fdad"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating Poems:   0%|          | 0/78 [00:07<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Input Prompt: \n",
      " فراوان به درویش دینار داد\n",
      "-------------------\n",
      "Generated Poem: \n",
      "فراوان به درویش دینار داد\n",
      " شاداد بادوادبادداد نهادنهاد یادزاند\n",
      "-------------------\n",
      "Label: \n",
      " همان خوردنیهای بسیار داد\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " که او را بود زندگانی دراز\n",
      "-------------------\n",
      "Generated Poem: \n",
      "که او را بود زندگانی دراز\n",
      " نیاز باز راز نازازبربم فرازت نماز\n",
      "-------------------\n",
      "Label: \n",
      " نشیند به شادی و آرام و ناز\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " ز چیزی که گفتند پیغمبران\n",
      "-------------------\n",
      "Generated Poem: \n",
      "ز چیزی که گفتند پیغمبران\n",
      " ورندکهببرسرگرانمجهانان\n",
      "-------------------\n",
      "Label: \n",
      " همان دادگر موبدان و ردان\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " همی شد گریزان تژاو دلیر\n",
      "-------------------\n",
      "Generated Poem: \n",
      "همی شد گریزان تژاو دلیر\n",
      " شیریر سیر زیربرسرازبدب\n",
      "-------------------\n",
      "Label: \n",
      " پسش بیژن گیو برسان شیر\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " یکی لشکر آمد بر ما به جنگ\n",
      "-------------------\n",
      "Generated Poem: \n",
      "یکی لشکر آمد بر ما به جنگ\n",
      " درنگ ننگ تنگنگمزبغ پلنگ چنگ\n",
      "-------------------\n",
      "Label: \n",
      " چو کلباد و نستیهن تیز چنگ\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " به ازبنده بندن بسال دراز\n",
      "-------------------\n",
      "Generated Poem: \n",
      "به ازبنده بندن بسال دراز\n",
      " نیازازبربدبم کم ببندند\n",
      "-------------------\n",
      "Label: \n",
      " به گنج جهاندار بردن نیاز\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " بیاراسته سیستان چون بهشت\n",
      "-------------------\n",
      "Generated Poem: \n",
      "بیاراسته سیستان چون بهشت\n",
      " کشت درشتشت بکندکهنهجهانبدبرست\n",
      "-------------------\n",
      "Label: \n",
      " گلش مشک سارابد و زر خشت\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " برو کفریننده یار تو باد\n",
      "-------------------\n",
      "Generated Poem: \n",
      "برو کفریننده یار تو باد\n",
      "بادوادزادادنهادداد یادندزاند\n",
      "-------------------\n",
      "Label: \n",
      " چو لهاک سیصد شکار تو باد\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " مرا از به و بتر آگه کنید\n",
      "-------------------\n",
      "Generated Poem: \n",
      "مرا از به و بتر آگه کنید\n",
      "ندازمیانهمهکهنهبهمب\n",
      "-------------------\n",
      "Label: \n",
      " ز بدها گمانیم کوته کنید\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " از آشوب ترکان و از رزم سخت\n",
      "-------------------\n",
      "Generated Poem: \n",
      "از آشوب ترکان و از رزم سخت\n",
      " درخت بخت تختبخت تاج رخت عاجاج گنج رنج\n",
      "-------------------\n",
      "Label: \n",
      " فرامرز را نیزه شد لخت لخت\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " می و گلشن و بانگ چنگ و رباب\n",
      "-------------------\n",
      "Generated Poem: \n",
      "می و گلشن و بانگ چنگ و رباب\n",
      " خوابازکهببرمبدند\n",
      "-------------------\n",
      "Label: \n",
      " گل و سنبل و رطل و افراسیاب\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " به بالای یک نیزه برف آیدت\n",
      "-------------------\n",
      "Generated Poem: \n",
      "به بالای یک نیزه برف آیدت\n",
      " وندکهنهبم گزندازبربد\n",
      "-------------------\n",
      "Label: \n",
      " بدو روز شادی شگرف آیدت\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " گزین کرد شمشیرزن سی هزار\n",
      "-------------------\n",
      "Generated Poem: \n",
      "گزین کرد شمشیرزن سی هزار\n",
      " کارزار سوار شهریار شکارار کار گاغسربم\n",
      "-------------------\n",
      "Label: \n",
      " که بودند شایستهٔ کارزار\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " چو آواز آن پاسبانان شنید\n",
      "-------------------\n",
      "Generated Poem: \n",
      "چو آواز آن پاسبانان شنید\n",
      " ناپدیدید ندیدکشیدبلزسپبدبمی شد\n",
      "-------------------\n",
      "Label: \n",
      " غمی گشت و زیشان دلش بردمید\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " سپهدار چین با فرستاده گفت\n",
      "-------------------\n",
      "Generated Poem: \n",
      "سپهدار چین با فرستاده گفت\n",
      "فت جفت نهزبرسربکهودنشست\n",
      "-------------------\n",
      "Label: \n",
      " که با شاه تو مشتری باد جفت\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " رباید همی این ازآن آن ازین\n",
      "-------------------\n",
      "Generated Poem: \n",
      "رباید همی این ازآن آن ازین\n",
      "مکنمپرمازکهنهجهانبدبمی\n",
      "-------------------\n",
      "Label: \n",
      " ز نفرین ندانند باز آفرین\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " بزرگی و فرزند کاووس شاه\n",
      "-------------------\n",
      "Generated Poem: \n",
      "بزرگی و فرزند کاووس شاه\n",
      " کلاه گاه سپاه جاهگاهکلاهسپغنهبنهاد\n",
      "-------------------\n",
      "Label: \n",
      " سر از بس هنرها رسیده به ماه\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " بران هر یکی دانه ها صد هزار\n",
      "-------------------\n",
      "Generated Poem: \n",
      "بران هر یکی دانه ها صد هزار\n",
      " کارزار سوارار شکارزار کار گاوسف زارخوار\n",
      "-------------------\n",
      "Label: \n",
      " بها بود بر دفتر شهریار\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " که آغاز و فرجام این رزمگاه\n",
      "-------------------\n",
      "Generated Poem: \n",
      "که آغاز و فرجام این رزمگاه\n",
      " سپاه کلاه گاهکلاهسپسربزرازبدب\n",
      "-------------------\n",
      "Label: \n",
      " شنیدی و دیدی بنزد سپاه\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " شکم گشت فربه و تن شد گران\n",
      "-------------------\n",
      "Generated Poem: \n",
      "شکم گشت فربه و تن شد گران\n",
      "انازبربرانسربازانگران\n",
      "-------------------\n",
      "Label: \n",
      " شد آن ارغوانی رخش زعفران\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " اگر ماند ایدر ز تو نام زشت\n",
      "-------------------\n",
      "Generated Poem: \n",
      "اگر ماند ایدر ز تو نام زشت\n",
      "دیوسربزرازهمهزکهبدب\n",
      "-------------------\n",
      "Label: \n",
      " بدانجا نیایی تو خرم بهشت\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " بگفتند با او بسی رزم پیل\n",
      "-------------------\n",
      "Generated Poem: \n",
      "بگفتند با او بسی رزم پیل\n",
      " انجمن کفنتنسردلبرکهممنند\n",
      "-------------------\n",
      "Label: \n",
      " که او اسپ را بفگند از دو میل\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " همی کرد نخچیر آهو نخست\n",
      "-------------------\n",
      "Generated Poem: \n",
      "همی کرد نخچیر آهو نخست\n",
      "ستازکهبدبربم وز\n",
      "-------------------\n",
      "Label: \n",
      " بر شیر و جنگ پلنگان نجست\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " ز ایوان برآمد یکی آفرین\n",
      "-------------------\n",
      "Generated Poem: \n",
      "ز ایوان برآمد یکی آفرین\n",
      " تخت تاج عاجاجروز بخت وسپبدب شاه سپاه\n",
      "-------------------\n",
      "Label: \n",
      " بران دادگر شهریار زمین\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " برانوش گفتا که ایران تراست\n",
      "-------------------\n",
      "Generated Poem: \n",
      "برانوش گفتا که ایران تراست\n",
      " راستاستست استردزسپبدب و\n",
      "-------------------\n",
      "Label: \n",
      " نصیبین و دشت دلیران تراست\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " که بودند یازان به خون پدر\n",
      "-------------------\n",
      "Generated Poem: \n",
      "که بودند یازان به خون پدر\n",
      " سر جگرسربگر هنر زر کمرندگران\n",
      "-------------------\n",
      "Label: \n",
      " ز تنهای ایشان جدا کرد سر\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " چوبهرام را خواند از راستی\n",
      "-------------------\n",
      "Generated Poem: \n",
      "چوبهرام را خواند از راستی\n",
      " وژی کاستیادیردیخسرجهانکهبمی\n",
      "-------------------\n",
      "Label: \n",
      " پدید آمد اندر دلش کاستی\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " سبک موبد موبدان را بخواند\n",
      "-------------------\n",
      "Generated Poem: \n",
      "سبک موبد موبدان را بخواند\n",
      " براند بماند نشاند خوانداندنشاندندفششگران فشز\n",
      "-------------------\n",
      "Label: \n",
      " بران جای خراد برزین نشاند\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " سپهدار ترکان ازان بترست\n",
      "-------------------\n",
      "Generated Poem: \n",
      "سپهدار ترکان ازان بترست\n",
      "ندکهبنهبدبرسرازسپ\n",
      "-------------------\n",
      "Label: \n",
      " کنون گاو پیسه به چرم اندرست\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " بسنده نبودش همین بد که کرد\n",
      "-------------------\n",
      "Generated Poem: \n",
      "بسنده نبودش همین بد که کرد\n",
      "وردردزبرسرسپبدبکه\n",
      "-------------------\n",
      "Label: \n",
      " همی رزم جوید بننگ و نبرد\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " بدو گفت جاماسپ کای شهریار\n",
      "-------------------\n",
      "Generated Poem: \n",
      "بدو گفت جاماسپ کای شهریار\n",
      " سوارار شکارزار کار گا خوارخواروسفسار\n",
      "-------------------\n",
      "Label: \n",
      " سخن بشنو از من یکی هوشیار\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " همی کرد زور ان برین این بران\n",
      "-------------------\n",
      "Generated Poem: \n",
      "همی کرد زور ان برین این بران\n",
      " زمین کین چینین دینانزمیننهند\n",
      "-------------------\n",
      "Label: \n",
      " گرازان و پیچان دو مرد گران\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " که بتخانه را هیچ نگذاشتی\n",
      "-------------------\n",
      "Generated Poem: \n",
      "که بتخانه را هیچ نگذاشتی\n",
      "ندزسربزرازبرازانچهبمی\n",
      "-------------------\n",
      "Label: \n",
      " کلید در پرده او داشتی\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " بیاراستندش به دیبای روم\n",
      "-------------------\n",
      "Generated Poem: \n",
      "بیاراستندش به دیبای روم\n",
      " بوموممغ دریغ وبنهاد\n",
      "-------------------\n",
      "Label: \n",
      " همه پیکرش گوهر و زر بوم\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " ز گاه منوچهر تا این زمان\n",
      "-------------------\n",
      "Generated Poem: \n",
      "ز گاه منوچهر تا این زمان\n",
      " گمان کمانگمانگرانوزانببربدازکهم\n",
      "-------------------\n",
      "Label: \n",
      " نبودی مگر بدتن و بدگمان\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " پرستنده با ماه دیدار گفت\n",
      "-------------------\n",
      "Generated Poem: \n",
      "پرستنده با ماه دیدار گفت\n",
      " جفتفتودزمرسردرازبدبم\n",
      "-------------------\n",
      "Label: \n",
      " که هرگز نماند سخن در نهفت\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " فرستاده آمد بر شاه نو\n",
      "-------------------\n",
      "Generated Poem: \n",
      "فرستاده آمد بر شاه نو\n",
      " یاد شادادباددادواد باد نهادنهاد دادزاد\n",
      "-------------------\n",
      "Label: \n",
      " گذشته شبی تیره از ماه نو\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " نیای تو زین خاندان زنده بود\n",
      "-------------------\n",
      "Generated Poem: \n",
      "نیای تو زین خاندان زنده بود\n",
      " برورد باد درد سرد زردزبر گردرد\n",
      "-------------------\n",
      "Label: \n",
      " پدر پیش بهرام پاینده بود\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " سر نامه بود از نخست آفرین\n",
      "-------------------\n",
      "Generated Poem: \n",
      "سر نامه بود از نخست آفرین\n",
      " تخت تاج بخت و عاجروززسپبدببخت\n",
      "-------------------\n",
      "Label: \n",
      " چنانچون بود رسم آیین و دین\n",
      "==================================================\n",
      "\n",
      "Input Prompt: \n",
      " چو آسوده گردید یکسر به بزم\n",
      "-------------------\n",
      "Generated Poem: \n",
      "چو آسوده گردید یکسر به بزم\n",
      " کمندکهبدبغ وزسپ\n",
      "-------------------\n",
      "Label: \n",
      " که زود آید اندیشهٔ روز رزم\n",
      "==================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def generate_poem(model, tokenizer, prompt, max_length=20,  top_k=1):\n",
    "    \n",
    "    # Decode the prompt\n",
    "    input_ids = tokenizer.encode( '<s>' + prompt, return_tensors=\"pt\").to(device)\n",
    "\n",
    "    # Generate poem\n",
    "    with torch.no_grad():\n",
    "        output = model.generate(input_ids, max_length=max_length,  top_k=top_k, num_beams=15, no_repeat_ngram_size=1, \n",
    "                                 attention_mask=torch.ones_like(input_ids),\n",
    "#                                 eos_token_id=tokenizer.eos_token_id,\n",
    "                                pad_token_id=tokenizer.pad_token_id, )\n",
    "#                                 bos_token_id=tokenizer.bos_token_id)\n",
    "\n",
    "    generated_poem = tokenizer.decode(output[0], skip_special_tokens=True)\n",
    "    return generated_poem\n",
    "\n",
    "# A function for pass the test data points (1st MESRA) to model and generate 2nd MESRA\n",
    "def generate_poems_for_test_set(model, tokenizer, test_dataloader):\n",
    "    model.eval()\n",
    "\n",
    "    generated_poems = []\n",
    "    \n",
    "    for batch in tqdm(test_dataloader, desc=\"Generating Poems\"):\n",
    "        inputs = batch[\"input_ids\"].to(device)\n",
    "        labels = batch[\"labels\"].to(device)\n",
    "        flag = 0\n",
    "        for i in range(len(inputs)):\n",
    "            flag+=1\n",
    "            input_prompt = tokenizer.decode(inputs[i].squeeze(), skip_special_tokens=True)\n",
    "\n",
    "            label = tokenizer.decode(labels[i].squeeze(), skip_special_tokens=True)\n",
    "\n",
    "            generated_poem = generate_poem(model, tokenizer, input_prompt)\n",
    "            generated_poems.append((input_prompt, generated_poem, label))\n",
    "            if flag ==40:\n",
    "                break\n",
    "        break\n",
    "    return generated_poems\n",
    "\n",
    "\n",
    "# Generate poems for the test set\n",
    "generated_poems = generate_poems_for_test_set(model, tokenizer, test_dataloader)\n",
    "\n",
    "# Print generated poems\n",
    "for input_prompt, generated_poem, label in generated_poems:\n",
    "    print(\"\")\n",
    "    print(f\"Input Prompt: \\n {input_prompt}\")\n",
    "    nl_index = len(input_prompt)\n",
    "    final_gen = '\\n' +generated_poem[:nl_index] + '\\n' + generated_poem[nl_index:]\n",
    "    print('-------------------')\n",
    "    print(f\"Generated Poem: {final_gen}\")\n",
    "    print('-------------------')\n",
    "    print(f\"Label: \\n {label}\")\n",
    "    print(\"=\" * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BLEU and Perplexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to calculate perplexity\n",
    "def calculate_perplexity(model, iterator, criterion):\n",
    "    model.eval()\n",
    "    total_loss = 0\n",
    "    total_tokens = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for batch in iterator:\n",
    "            src = batch.src.to(device)\n",
    "            trg = batch.trg.to(device)\n",
    "\n",
    "            output = model(src, trg[:, :-1])\n",
    "            loss = criterion(output.view(-1, output.shape[-1]), trg[:, 1:].contiguous().view(-1))\n",
    "\n",
    "            total_loss += loss.item()\n",
    "            total_tokens += trg[:, 1:].ne(TRG.vocab.stoi[\"<pad>\"]).sum().item()\n",
    "\n",
    "    perplexity = math.exp(total_loss / total_tokens)\n",
    "    return perplexity\n",
    "\n",
    "# Define a function to calculate BLEU score\n",
    "def calculate_bleu(model, iterator, trg_field):\n",
    "    model.eval()\n",
    "    references = []\n",
    "    hypotheses = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for batch in iterator:\n",
    "            src = batch.src.to(device)\n",
    "            trg = batch.trg.to(device)\n",
    "\n",
    "            output = model.generate(src)\n",
    "            trg_str = trg_field.reverse(trg)\n",
    "            output_str = trg_field.reverse(output)\n",
    "\n",
    "            references.extend(trg_str)\n",
    "            hypotheses.extend(output_str)\n",
    "\n",
    "    return corpus_bleu([[ref] for ref in references], hypotheses)\n",
    "\n",
    "# Create iterators\n",
    "test_iterator = BucketIterator(\n",
    "    test_data,\n",
    "    batch_size=128,\n",
    "    sort_key=lambda x: len(x.src),\n",
    "    sort_within_batch=True,\n",
    "    device=device)\n",
    "\n",
    "# Calculate perplexity\n",
    "criterion = torch.nn.CrossEntropyLoss(ignore_index=TRG.vocab.stoi[\"<pad>\"])\n",
    "test_perplexity = calculate_perplexity(my_model, test_iterator, criterion)\n",
    "print(f\"Test Perplexity: {test_perplexity:.4f}\")\n",
    "\n",
    "# Calculate BLEU score\n",
    "test_bleu = calculate_bleu(my_model, test_iterator, TRG.vocab)\n",
    "print(f\"Test BLEU Score: {test_bleu:.4f}\")"
   ]
  }
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